Predicting chronic kidney disease progression

ABSTRACT

Systems, methods, and computer-readable media are provided for identification of patients having an elevated near-term risk of chronic kidney disease progression, including quantitatively predicting whether or not an elevated risk of progression of Stage 3 or Stage 4 chronic kidney disease is likely within a time interval of up to 36 months subsequent to computing the prediction. Based on the prediction, appropriate care providers may be notified so that the risk of CKD progression may be mitigated. In some embodiments, serial measurements are obtained of urine osmolality, and a challenge with an AVP V2 antagonist and serum sodium concentration is provided. From a time series based on the serial measurements, estimates of each variable&#39;s velocity and/or doubling-time may be determined. These values then may be combined via a multivariable mathematical model for providing a leading indicator of near-term future abnormalities in kidney function.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Nonprovisional applicationSer. No. 15/276,537, filed Sep. 26, 2016 which claims the benefit ofU.S. Provisional Patent Application No. 62/232,964, filed Sep. 25, 2015,each of which is herein incorporated by reference in its entirety.

BACKGROUND

Chronic kidney disease (hereinafter referred to as “CKD”) is increasingin epidemiologic and economic importance. In 2010, CKD resulted in735,000 deaths, a substantial increase compared to 400,000 deathsrecorded in 1990. In the U.S., the Centers for Disease Control andPrevention found that between 1999 and 2004, CKD affected an estimated16.8% of adults 20 years of age and older. Presently, 26 millionAmerican adults have CKD. In the U.K., the National Health Service hasestimated that 8.8% of the population of Great Britain and NorthernIreland have symptomatic CKD. In 2011, total Medicare expenditures forCKD in the United States exceeded 45 billion. Additionally, diagnoses ofcardiovascular-related comorbidities (e.g., hypertension,hypercholesterolemia, diabetes, and cardiovascular disease) increase asCKD becomes worse. Annual health spending increases with increasingseverity and comorbidities of CKD, and quality of life diminishes. As aresult, accurate diagnosis and prediction of CKD are becomingsignificantly more important for controlling disease outcomes, and thecost of treatment.

SUMMARY

The present disclosure relates generally to systems, methods, andcomputer-readable media for identifying patients having an elevatednear-term risk of CKD progression (e.g., a risk of progressing fromStage 3 CKD to Stage 4 CKD). Embodiments of the present disclosure arefurther directed to event prediction, risk stratification, andoptimization of the assessment, communication, and decision-makingrelated to CKD in order to prevent, treat, and/or manage CKD in humans.In one embodiment, a platform for embedded decision support in anelectronic health record (hereinafter referred to as “EHR”) system isprovided. Further embodiments of the present disclosure facilitatemonitoring human patients to provide quantitative prediction of a degreeof risk of CKD progression within a selected time interval. For example,a risk of progression from Stage 3 CKD to Stage 4 CKD in a human patientup to 36 months subsequent to computing the prediction may be provided.Additionally, upon determining a degree of risk of progression of CKD,additional actions, such as informing a care provider clinicians'decisions and renoprotective interventions, may be performed to reducethe risk of progression of CKD and concomitant morbidity in patients.

Thus, an aim of embodiments of the present disclosure relates toautomatically identifying persons who are at risk for CKD progression. Afurther aim of some embodiments of the present disclosure is to reliablydistinguish between patients with Stage 3-5 CKD at first classificationwhose disease (a) remained stable, (b) progressed, or (c) improved in aquantifiable manner to enable accurate decision-making concerningoptimal treatment to reduce or delay progression of CKD in those in whomit is probable within a time horizon of up to 36 months. In someembodiments, persons at risk for CKD progression may be identifiedthrough the use of serial laboratory measurements and temporalproperties of multivariable time series determined from themeasurements. The measurements and predictive algorithms may be used ingeneral acute-care venues and afford a degree of robustness againstvariations in individual physiology, comorbid diagnoses, and severity ofillness. Some embodiments further provide a leading indicator ofnear-term future abnormalities, proactively notifying clinicians caringfor a patient, and providing the care providers with sufficient advancenotice to enable effective preventive maneuvers to be undertaken when anelevated risk of CKD progression is detected. Additionally, in someembodiments, an indicator may provide notice of the effectiveness (orlack thereof) of alternative therapeutic regimens, and may assist infurther decision-making in the medical management of the patient. As aresult, a proactive intervention may be implemented for any identifiedat-risk persons.

Accordingly, in some embodiments, serial measurements of urineosmolality may be obtained for a patient following a challenge with atest dose of an AVP V2 antagonist, such as tolvaptan and/or serum sodiumconcentration. The measurements may be combined via a multivariablemathematical model and used to provide a leading indicator of near-termresponsiveness to the AVP V2 antagonist regimen. In this way,embodiments of the present disclosure facilitate predictionclassification or decision-support alert signals to be provided atlogistically convenient times far enough in advance of progression toStage 5 CKD to allow for effective preventive intervention in a majorityof cases. Moreover, some embodiments of the present disclosure usecommonly available laboratory tests, which may be performed serially toprovide data for predictive processes. Thus, the timely determining of,for example, a 36-month predicted likelihood of CKD progression isperformed in such a manner so as not to be unduly dependent on scarce orexpensive resources, increasing convenience and applicability acrosswidespread populations.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present disclosure are described in detail below withreference to the attached figures, which are intended to be exemplaryand non-limiting in nature, wherein:

FIGS. 1A-1B depict aspects of an illustrative operating environmentsuitable for practicing an embodiment of the present disclosure;

FIG. 2 depicts a block diagram of a method for predicting CKD riskand/or progression in an individual, in accordance with an embodiment ofthe present disclosure;

FIG. 3 depicts different stages of CKD and their corresponding estimatedglomerular filtration rates, in accordance with an embodiment of thepresent disclosure;

FIG. 4 depicts a Receiver Operating Characteristic (ROC) curverepresenting the accuracy and discriminating classificatory capacity ofthe present technology in a cohort of 49 subjects, in accordance with anembodiment of the present disclosure;

FIG. 5 depicts one exemplary embodiment of a computer program routineused for predicting risk and/or progression of CKD in an individual, inaccordance with an embodiment of the present disclosure; and

FIGS. 6-7 depict a flow diagram of methods for predicting CKD riskand/or progression in an individual, in accordance with an embodiment ofthe present disclosure.

DETAILED DESCRIPTION

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope hereof. Rather,the claimed subject matter might also be embodied in other ways, toinclude different steps or combinations of steps, similar to thosedescribed in this document, and in conjunction with other present orfuture technologies. Moreover, although the terms “step” and/or “block”may be used herein to connote different elements of methods employed,the terms should not be interpreted as implying any particular orderamong or between various steps or blocks disclosed herein unless andexcept when the order of individual steps is explicitly described andrequired.

As one skilled in the art will appreciate, embodiments of the technologymay be embodied as, among other things, a method, a system, and/or a setof instructions embodied on one or more computer-readable media.Accordingly, the embodiments may take the form of a hardware embodiment,a software embodiment, and/or an embodiment combining software andhardware. In one embodiment, a computer-program product that includescomputer-usable instructions embodied on one or more computer-readablemedia is provided.

Computer-readable media may include any available media that can beaccessed by a computing device, and includes both volatile andnon-volatile media, as well as removable and non-removable media. By wayof example, and not limitation, computer-readable media may includemedia implemented in any method or technology for storing information,including computer storage media and communication media. Computerstorage media includes both volatile and nonvolatile, as well asremovable and non-removable media, implemented in any method ortechnology for storage of information such as computer-readableinstructions, data structures, program modules, or other data. Computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the desired information and which canbe accessed by a computing device. Computer storage media does notcomprise signals per se. Communication media typically embodiescomputer-readable instructions, data structures, program modules, orother data in a modulated data signal, such as a carrier wave or othertransport mechanism, and includes any information delivery media. Theterm “modulated data signal” describes a signal that has one or more ofits characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media, such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared, or other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

In brief, at a high level, this disclosure describes, among otherthings, methods and systems for identifying persons who are at risk forprogression of CKD, or a degree of risk of such a progression. Inparticular, automatically identifying patients having an elevatednear-term risk of CKD progression is provided. Event prediction, riskstratification, and optimization of the assessment, communication, anddecision-making to prevent, treat, and/or manage CKD in humans is alsoprovided, and, in one embodiment, takes the form of a platform forembedded decision support in an EHR system. Determining a risk of CKDprogression may include analyzing serial laboratory measurements usingcommonly available laboratory tests, such as urine osmolality, followinga challenge with a test dose of an AVP V2 antagonist such as tolvaptanand serum sodium concentration. From these measurements, a multivariabletime series may be determined and used for generating a multivariablemathematical model for predicting CKD progression. The measurements andpredictive model may be suitable for application in general acute-carevenues and afford a degree of robustness against variations inindividual physiology, comorbid diagnoses, and severity of illness.Moreover, some embodiments provide a leading indicator of near-termfuture abnormalities, proactively notifying clinicians caring for apatient, and providing care providers with sufficient advance notice toenable effective preventive maneuvers to be undertaken. Additionally, insome embodiments, the indicator may provide notice of the effectiveness(or lack thereof) of alternative therapeutic regimens, and assist infurther decision-making in the medical management of the patient.

Some embodiments of the present disclosure may utilize and/or require aslittle as two time points (serial laboratory measurements) from a timeseries to establish a risk of CKD progression in a patient. In addition,if two or more such values are available, then estimates of theprobability of responsiveness to an agent or multi drug regimen by thepatient may be determined from the time series. De-noised values may becombined using a multi-variable mathematical model. In some embodiments,this may take the form of a logistic regression equation. In otherembodiments, the evidence-combining may be implemented via a neuralnetwork, a support vector machine, and/or other methods, such as areknown to those practiced in the art. In each of these embodiments, aleading indicator of near-term responsiveness to a regimen may beprovided. Furthermore, in one exemplary embodiment, a device isintegrated with case-management software and/or an electronic healthrecord decision-support system.

By way of example and not limitation, a user using an embodiment of thepresent technology may provide or withhold AVP V2 antagonist orTNF-alpha inhibitor to a patient with a greater degree of confidence, inaddition to alternative interventions. In this exemplary embodiment, thecomputer system may include an application which, when executed,receives user data from a device, calculates a plurality of time serieslaboratory test measurements, combines the plurality of time serieslaboratory test measurements in a mathematical model, and communicatesthe composite results to a clinician user, case-management software,decision-support system, and/or EHR system, in addition to otherpossible recipients. For example, the system may notify the user, theuser's health plan, EHR decision-support systems, and/or personal healthrecord systems with a message, electronic mail, a call, HTTP, SMS textmessage, or other form of electronic or radio frequency communication,indicating that the user may be likely to benefit from adisease-modifying medication or treatment regimen. This enables the careproviders to take appropriate measures, including determining insurancecoverage for the regimen, personnel or medication allocationrequirements, or other aspects.

As described in other sections of this disclosure, CKD is increasing inepidemiologic and economic importance in developed nations, and totalMedicare expenditures for CKD in 2011 exceeded 45 billion. Further,annual health spending increases with increasing severity andcomorbidities of CKD, and quality of life diminishes. Thus, it isadvantageous to prevent or delay progression of CKD to the more severe,later stages to the greatest extent possible. This includes reducing ortracking CKD with a high degree of accuracy up to end-stage renaldisease (ESRD), at which point renal replacement therapy (dialysis orkidney transplantation) may be needed. The prevalence of Stage 3 toStage 5 CKD in 2010 was 5.9%. In patients with Stage 3-5 CKD at firstclassification, the percentage of patients for which the disease (a)remained stable, (b) progressed, or (c) improved in 2010 wasapproximately 50%, 10-15%, and 25-30% of patients, respectively.

CKD also exhibits significant ethnic variation in its occurrence andprogression, mostly due to increased prevalence and severity ofhypertension. As an example, 37% of ESRD cases in African Americans canbe attributed to high blood pressure, compared with 19% amongCaucasians. Treatment efficacy also differs between racial groups.Administration of anti-hypertensive drugs generally halts diseaseprogression in Caucasian populations, but has little effect in slowingrenal disease among African American populations, and additionaltreatments, such as bicarbonate therapy, are often required.

Once patients at risk for CKD progression have been identified, avariety of therapeutic modalities may be used to alter a course of CKD,and/or slow its progression. For example, optimal diabetes therapy(HbA1c target 7%), reduction of proteinuria, aldosterone blockade, andthe use of ACEI/ARB antihypertensive therapies may be beneficial, aswell as limiting dietary sodium, limiting protein intake, and bodyweight reduction. Proper treatment for elevated serum phosphorus andparathyroid hormone levels can delay appearance of comorbidities thatexacerbate or accelerate progression of CKD. Correction of anemia witherythropoietin therapy is also beneficial in advanced CKD withhemoglobin goals in the 10-12 g/dL range. Additionally, treatment ofacidosis may improve CKD progression, and suggests that increasing serumbicarbonate to greater than 20 mmol/L may be beneficial. Approaches toaltering the course of CKD by targeting fibroblast growth factor 23(FGF-23), transforming growth factor β (TGF-β), tumor necrosis factoralpha (TNF-α), neprilysin, and nuclear factor-erythroid-2-related factor2 (Nrf2) level reductions also may be considered as modalities forreducing and controlling CKD progression.

Use of biomarkers may enable characterizing the biological basis for theheterogeneity of an individual's clinical course and their personalizedresponse to specific treatments, particularly in cancer. The ability tomolecularly characterize human diseases presents new opportunities todevelop more effective treatments, and also, new challenges for thedesign and analysis of clinical trials. ‘Personalized medicine’ is amodel for optimizing therapeutics. ‘Personalization’ posits that thecustomization of treatment for individual patients can deliver superiorclinical outcomes, with improved safety and cost-effectiveness. In sucha model, diagnostic tests are essential for selecting the safest andmost efficacious treatments, as well as for choosing a dose oradministration schedule for the same that best matches the pharmacologicand pathophysiologic particulars of an individual undergoing suchtreatment. The term ‘companion diagnostics’ may be used to describe suchtests, with molecular assays that measure the levels of specific solubleanalytes, proteins, and/or specific gene mutations being used to providea specific therapy for an individual by stratifying a disease status,selecting a proper medication regimen, and tailoringdosages/administration of selected therapeutics and treatments.Companion diagnostics may be used to aid clinical decision-making toidentify patients who are most likely to respond to particulartreatments and to identify patients who almost certainly will notbenefit from particular treatments. ‘Companion diagnostics’ (CoDx) mayinclude univariable tests that determine the presence or absence of areceptor that is pertinent to the mechanism of action of the associatedtherapeutic, or may include univariable tests that determine the levelof one particular analyte. Other CoDx's may be ‘in vitro diagnosticmultivariable index assays’ (IVDMIAs). In this connection, multivariablephenotypic profiling may be as important as genotypic profiling indevising personalized medicine treatments.

Further, in some instances, patient therapy may be improved through theidentification of targets and surrogate molecular signatures that canhelp direct appropriate treatment regimens in a patient while takinginto account treatment efficacy and drug safety. In particular, patientbiofluids or biopsy tissue may be isolated and analyzed for genetic,immunohistochemical, and/or soluble markers to determine if a predictivebiomarker signature (e.g., altered concentration of analyte, mutatedgene product, differentially expressed protein or pattern of multipleproteins, altered cell surface antigen, etc.) exists as a possibleconsideration for selecting optimal treatment. These biomarkers may bedrug-specific targets and/or differentially expressed nucleic acids,proteins, and/or cell lineage profiles that can directly affect thepatient's disease tissue and/or immune response to a therapeuticregimen.

Improvements in diagnostics that can prescreen predictive responsebiomarker profiles may also be used to optimize patient therapy viamolecularly defined, disease-specific treatment. Conversely, patientslacking predictive response biomarkers may no longer needlessly beexposed to drugs that are unlikely to provide clinical benefit, whichcan enable patients to pursue other therapeutic options and loweroverall healthcare costs by avoiding futile treatment. But while patientmolecular profiling offers a powerful tool for directing treatmentoptions, the difficulty in identifying disease-specific targets orpredictive biomarker signatures that stratify a significant fractionwithin a disease indication remains challenging.

However, according to an embodiment of the present disclosure, a patientcan be predetermined in real time as to whether or not their CKD can bespecifically ameliorated by a drug-linked diagnostic vector. Despitegrowing success in the treatment of CKD achieved with the use ofmolecular targeted therapy, resistance seems to develop to virtually allof the drugs at some point in time. One way to suppress or delaydevelopment of resistance might be through the use of combinationtherapy. For example, a combined regimen of an AVP V2 antagonist and aTNF-alpha inhibitor may offer synergistic benefits, compared to the sameor either therapeutic classes of disease-modifying agents usedindividually.

Recent trends are moving away from the univariable ‘one biomarker: onedrug’ companion diagnostic scenario, which has characterized the pasttwo decades of targeted drug development, toward a more integratedapproach with multiple biomarkers and multi-drug regimens. This ‘newparadigm’ may pave the way for the introduction of multiplexingstrategies using IVDMIAs that utilize multivariable phenotyping as wellas gene expression arrays and next-generation sequencing. This holds notonly for cancer treatment, but for the treatment of CKD and otherchronic diseases as well.

Advances in the understanding of the biology of kidney disease as wellas advances in diagnostic technologies, such as the advent of affordablehigh-resolution DNA sequencing, have had a major impact on the approachto identification of specific alterations in a given patient's conditionthat could be used as a basis for selecting a CKD treatment, and hence,the development of companion diagnostics. Presently, there are no such‘companion diagnostics’ in CKD, even though there are a number ofreceptor-targeted medications that have annual-cost-of-therapy pricetags exceeding $50,000 per year, which is sufficiently high to meritsuch a companion diagnostic test.

Notably, the action of some disease-modifying therapies (e.g.,anti-TNF-alpha agents) may only be evident over a period of many months,while the effect of other disease-modifying therapies may be evidenceright away. In that regard, certain biomarkers that characterize anindividual's kidney status and responsiveness to one class of agent(e.g., AVP V2 antagonists) may indirectly serve as surrogate measures ofresponsiveness to other agents whose mechanism of action is differentbut which nonetheless depend for their effectiveness on a kidney inwhich CKD has not progressed beyond a certain point.

Accordingly, some embodiments of the present disclosure involve justsuch a surrogate measure or multivariable predictor. In this connection,the antidiuretic hormone vasopressin may be used for regulating freewater clearance in normal physiology. However, vasopressin may havedeleterious effects on the kidney. Vasopressin is elevated in animalsand patients with CKD. Suppression of vasopressin activity reducesproteinuria, renal hypertrophy, glomerulosclerosis andtubulointerstitial fibrosis in animal models. The potential detrimentalinfluence of vasopressin may be mediated by its effects on mesangialcell proliferation, renin secretion, renal hemodynamics, and bloodpressure. Thus, vasopressin response may relate to CKD progression ingeneral and to autosomal dominant polycystic kidney disease inparticular. This has led to, over the past several years, thepossibility that interventions directed at lowering vasopressinactivity, for example by the administration of vasopressin receptorantagonists, may be beneficial in treating CKD.

Tolvaptan, a selective vasopressin V2 receptor antagonist, may also slowthe increase in total kidney volume and the decline in kidney function.However, it is as yet unclear (a) which patients are likely to benefitfrom tolvaptan and which will not, (b) which dose of tolvaptan isoptimal, or (c) whether tolvaptan is able to delay progression of CKD toESRD.

After several decades during which little attention was paid tovasopressin and/or urine concentration in clinical practice, interest invasopressin has renewed with the availability of new, potent, and orallyactive vasopressin receptor antagonists—the vaptans—and with the resultsof epidemiological studies evaluating copeptin (a surrogate marker ofvasopressin) in large population-based cohorts. Whether a selectiveblockade of the different vasopressin receptors may provide therapeuticbenefits beyond their present indication in hyponatremia requires newclinical trials at the present time.

Evidence is accumulating that arginine vasopressin (AVP) type 2 (V2)antagonist medications, such as tolvaptan, may normalize hyponatremiaand ameliorate progression of CKD in patients who have comorbid heartfailure (CHF). Many such patients are refractory to even high-dose loopdiuretics and are unable to produce dilute urine. However, AVPantagonist therapy is only effective in a percentage of CKD-CHFpatients. Furthermore, AVP antagonist therapy is found to be effectivein a considerable percentage of CKD patients who do not have CHF. It istherefore valuable to have diagnostic and prognostic means to identifyresponders and non-responders to tolvaptan or other AVP antagonistdrugs. This is true, in particular, because the current cost of AVPantagonist therapy is high (i.e., greater than $50,000 per year in theU.S. for a single patient) and access to these medications is tightlyrestricted.

The matter is made further complex by the fact thatregulatory-agency-approved on-label clinical indications at the presenttime state that AVP antagonists are indicated only in the condition ofhyponatremia (low concentration of sodium in the blood) and then only inthe event that the cause of the hyponatremia is determined to be thesyndrome of inappropriate antidiuretic hormone secretion (SIADH).However, observational data accruing from measurements made in patientswho received AVP antagonists off-label, even though they were nothyponatremic or only borderline hyponatremic, indicate that AVPantagonists may confer benefits in terms of preserving or restoringkidney function in CKD patients by a mechanism as yet unknown that maybe independent of AVP antagonists' effects on sodium metabolism. Afurther aspect has to do with the frequency with which hyponatremia iscaused or exacerbated by antidepressant therapy. In CKD, comorbidclinical depression and prescribing of antidepressants are commonplace.

Patients in earlier stages of CKD (Stages 1-3) bear increased risks forprogression to Stage 5 CKD (i.e., ESRD), at which time permanentdialysis therapy or renal transplantation is the only option. Thus,there is a compelling need to predict and prevent CKD efficaciously and,in those for whom prevention is not possible or successful, to undertakeeffective treatment of CKD as quickly as possible. In some approaches,the principle clinical tools used to detect CKD have been serialmeasurement of serum creatinine (Cr), blood urea nitrogen (BUN), certainother urine biochemical markers, and measurement of urine output volumeper unit of time. However, accurate prediction using such markers isunreliable based on (a) inadequate statistical sensitivity andspecificity for the purpose of predicting progression of CKD, and (b)the requirement for prolonged follow-up and repeated measurements over aperiod of months before guidance is obtained as to a rate of progressionof CKD in a particular patient.

The established CKD progression end point of ESRD, or a doubling ofserum creatinine concentration (corresponding to a change in estimatedglomerular filtration rate [eGFR] of ˜57% or greater) is a late event.Bicarbonate concentrations are likewise a lagging indicator. Incontrast, embodiments described herein may provide a leading indicator,which may facilitate characterizing prognosis, and also, help to guidetherapy, so as to retard the progression of CKD.

Biomarkers such as ADMA and KIM-1 have recently shown promise as leadingindicators of CKD progression. However, diagnostics based on measurementof such markers have not yet received regulatory approval and, even whenapproval is forthcoming, the availability of such tests may likely belimited, particularly in smaller and community-based settings. Thus,another advantage of some embodiments of the present disclosure is toprovide diagnostics that utilize biomarkers that are inexpensive andalready broadly available.

The role of uric acid (UA) as a biomarker for the progression of CKDremains controversial. Experimental and clinical studies indicate thatUA is associated with several risk factors associated with CKD,including diabetes, hypertension, oxidative stress, inflammation, andhyperuricemia. UA could also be considered as a common dominator linkingCKD and cardiovascular disease. Notably, the impact of serum UA levelson the survival of CKD, dialysis patients, and renal transplantrecipients is also a matter of debate, as there are conflicting resultsfrom clinical studies. At present, there is no definite data whether UAis causal, compensatory, coincidental, or if it is only an epiphenomenonin these patients.

Based on the prominence of microvascular changes in the causation of CKDprogression, Baumann and colleagues suggest that retinal photography incombination with albuminuria determination may be useful for riskstratification with respect to renal disease progression in patientswith CKD Stages 2-4. However, retinal arteriolar narrowing is confoundedby aging, hypertension, CKD, and other non-renal vascular processes.Additionally, accurate measurement of retinal arteriolar narrowing byophthalmologists requires specialized equipment that is not routinelyavailable in ambulatory clinics.

Moreover, some genomics-based or proteomics-based approaches involvecumbersome, complex, expensive, and/or invasive instrumentation. Otherrecently introduced methods involve measurements, such as genomic orproteomic laboratory tests, that are not widely available, and that haveperformance turnaround times of many hours or days before the resultsand predictions are available for use. As a result, a prediction orclassification may not be timely with respect to interventions aimed atpreventing the predicted progression.

Accordingly, advantages of predictive and diagnostic methods accordingto embodiments of the present disclosure described herein arise not onlyin prevention of CKD progression but also in the management of CKD ingeneral. Essentially, some such embodiments constitute a specializedtype of so-called ‘companion diagnostics’ or IVDMIAs that can help toguide optimal selection of therapeutic treatments.

In light of the foregoing, an improved predictive-preventive method andsystem for management and treatment of CKD has been devised. Embodimentsof the methods and systems, including prediction classification and/ordecision-support alert signals emitted by the system, are provided atlogistically convenient times, and also far enough in advance ofprogression to Stage 5 CKD to allow for effective preventiveintervention in many cases. In some embodiments, the systems and methodsinclude the use of commonly available laboratory tests performed in aserial fashion. For example, the timely determining of a 36-monthpredicted likelihood of CKD progression may be performed in such amanner so as not to be unduly dependent on scarce or expensiveresources, making it more effective, widely applicable, and convenient,which may also result in more efficient treatment protocols with betteroutcomes.

Referring now to the drawings in general, and initially to FIG. 1A, anexemplary operating environment 100 that is suitable for practicing anembodiment of the present technology is provided. Certain items inblock-diagram form are provided for referencing something consistentwith the nature of this disclosure, rather than to imply that a certaincomponent is or is not part of a certain device. Similarly, althoughsome items are depicted in the singular form, they may be plural as well(e.g., what is shown as one data store might really be multiple datastores distributed across multiple locations). In this respect, showingevery variation of each item might obscure the invention, and thus, forreadability, items are provided in the singular, while the plural isalso fully contemplated in every instance.

As shown in FIG. 1A, exemplary operating environment 100 provides anaspect of a computerized system for compiling and/or running aspects ofthe present technology, including collecting and analyzing unstructuredtext data from electronic health record(s) to assess the texts as totopical or concept-oriented expressions they contain that arestatistically similar to those associated with various clinicalconditions or diagnoses, to identify which condition/diagnosis-orientedclusters the texts most closely resemble, if any, and to notify theresponsible clinicians of those determinations. The system may furthersuggest consideration of those conditions or diagnoses as part of theconstellation of differential diagnoses pertinent to the management of acurrent patient.

Environment 100 includes one or more EHR systems, such as hospital EHRsystem 160, which is communicatively coupled to network 175, which iscommunicatively coupled to computer system 120. In some embodiments,components of environment 100 that are shown as distinct components maybe embodied as part of, or within, other components of environment 100.For example, EHR system 160 may comprise one or a plurality of EHRsystems, such as hospital EHR systems, health information exchange EHRsystems, ambulatory clinic EHR systems, psychiatry/neurology EHRsystems, and/or other systems that may be implemented in computer system120. Similarly, EHR system 160 may perform functions for two or more ofthe EHR systems (which are not shown).

Network 175 may comprise the Internet, one or more public networks, oneor more private networks, and/or any other communications networks, suchas a cellular network or other wireless communications network forfacilitating communication among devices connected through the network.In some embodiments, network 175 may be determined based on factors suchas the source and destination of the information communicated overnetwork 175, the path between the source and the destination, and/or thenature of the information. For example, intra-organization or internalcommunication may use a private network or virtual private network(VPN). Moreover, in some embodiments, items shown communicativelycoupled to network 175 may be directly communicatively coupled to otheritems shown communicatively coupled to network 175.

In some embodiments, operating environment 100 may include a firewall(not shown) between a first component and network 175. In suchembodiments, the firewall may reside on a second component locatedbetween the first component and network 175, such as on a server (notshown), or reside on another component within network 175, or may resideon or as part of the first component.

Embodiments of EHR system 160 include one or more data stores of healthrecords, which may be stored on data store 121, and may further includeone or more computers or servers that facilitate the storing andretrieval of the health records. In some embodiments, EHR system 160 maybe implemented as a cloud-based platform or may be distributed acrossmultiple physical locations. EHR system 160 may further include recordsystems, which store real-time or near real-time patient (or user)information, such as, for example, wearable, bedside, and/or in-homepatient monitors. Although FIG. 1A depicts an exemplary EHR system 160,it is contemplated that an embodiment of the present disclosure may relyon user manager or patient manager 140 and/or monitor 141 for storingand retrieving patient record information, such as information acquiredfrom monitor 141.

Example operating environment 100 further includes user/clinicianinterface 142 communicatively coupled through network 175 to the EHRsystem 160. Although environment 100 depicts an indirect communicativecoupling between the interface 142 and the EHR system 160 through thenetwork 175, it is contemplated that an embodiment of the interface 142is directly communicatively coupled to the EHR system 160. An embodimentof the interface 142 takes the form of a user interface operated by asoftware application or a set of software applications on a clientcomputing device, such as a personal computer, laptop, smartphone,and/or tablet computing device. In an embodiment, the applicationincludes the PowerChart® software manufactured by Cerner Corporation. Inan embodiment, the application is a Web-based application or applet. Aprovider clinician application facilitates accessing and receivinginformation from a user or health care provider about a specific patientor set of patients for which the likelihood(s) of future events, such asacute risk of deterioration, are determined according to the embodimentspresented herein.

Embodiments of the interface 142 also facilitate accessing and receivinginformation from a user or health care provider about a specific patientor population of patients, including patient history, health careresource data, variables measurements, time series, predictions(including plotting or displaying the determined outcome and/or issuingan alert), or other health-related information. Embodiments of theinterface 142 may also facilitate the display of results,recommendations, and/or orders, for example. In an embodiment, interface142 also facilitates receiving orders for the patient from aclinician/user based on the results of monitoring and predictions.Interface 142 may also be used for providing diagnostic services orevaluation of the performance of various embodiments.

An embodiment of patient manager 140 takes the form of a user interfaceand application, which may be embodied as a software applicationoperating on one or more mobile computing devices, tablets, smartphones,front-end terminals in communication with back-end computing systems,laptops, and/or other computing devices. In an embodiment, manager 140includes a Web-based application or a set of applications usable tomanage user services provided by an embodiment of the technology. Forexample, in an embodiment, manager 140 facilitates processing,interpreting, accessing, storing, retrieving, and communicatinginformation acquired from monitor 141, EHR system 160, or storage 121,including candidate diagnoses or conditions determined by embodiments ofthe technology as described herein. In an embodiment, manager 140 sendsa notification (such as an alarm or other indication) directly touser/clinician interface 142 through network 175. In an embodiment,manager 140 sends a maintenance indication to provider clinicianinterface 142. In one embodiment of the manager 140, an interfacecomponent may be used to facilitate access by a user (including aclinician/caregiver or patient) to functions or information on themonitor 141, such as operational settings or parameters, useridentification, user data stored on monitor 141, diagnostic services,and/or firmware updates for monitor 141, for example.

As shown in example environment 100, in one embodiment, manager 140 iscommunicatively coupled to monitor 141 and to network 175. In anembodiment, patient monitor 141 communicates via network 175 to computersystem 120 and/or to provider clinician interface 142.

An embodiment of monitor 141 (sometimes referred to herein as apatient-interface component) comprises one or more sensor componentsoperable to acquire clinical or physiological information about apatient, such as various types of physiological measurements,physiological variables, or similar clinical information associated witha particular physical or mental state of the patient, which may beacquired periodically or as one or more time series. In one embodiment,monitor 141 comprises sensors for obtaining and analyzing the serialmeasurements of urine osmolality and serum sodium concentration. In someembodiments, monitor 141 comprises a patient bedside monitor, such asthose used in hospitals to monitor patients. In an embodiment, one ormore sensor components of monitor 141 may comprise a user-wearablesensor component or sensor component integrated into the patient'senvironment. Examples of sensor components of monitor 141 include asensor positioned on an appendage (on or near the user's head, attachedto the user's clothing, worn around the user's head, neck, leg, arm,wrist, ankle, finger, etc.), a skin-patch sensor, an ingestible orsubdermal sensor, a sensor component integrated into the user's livingenvironment (including the bed, pillow, and/or bathroom), and sensorsoperable with or through a smartphone carried by a user, for example.

It is also contemplated that the clinical or physiological informationabout a patient, such as the monitored variables and/or clinicalnarratives regarding the patient, used according to the embodiment ofthe present technology disclosed herein, may be received from humanmeasurements, human observations, and/or automatically determined bysensors in proximity to the patient. For example, in one embodiment, anurse periodically measures a patient's blood pressure and enters themeasurement and/or observations via manager 140 or interface 142. Inanother example, a nurse or caregiver enters one or more progress notesfor an in-patient via manager 140 or interface 142. Similarly, valuesfor serial measurements of urine osmolality and serum sodiumconcentration may be entered via manager 140 or interface 142.

Examples of physiological variables monitored by monitor 141 can includeurine osmolality and serum sodium concentration, as described herein.Additionally, in some embodiments, physiological variables monitored bymonitor 141 may include, by way of example and not limitation, heartrate, blood pressure, oxygen saturation (SaO2), central venous pressure,other vital signs, and/or any other type of measureable, determinable,and/or observable physiological or clinical variable or characteristicassociated with a patient, which in some embodiments may be used forforecasting a future value (e.g., of the measured variable, a compositevariable based on one or more of the measured variables, or anotherfactor determined at least in part from one or more of the measuredvariables, etc.) of a patient in order to facilitate clinicaldecision-making In one further embodiment, a monitor, such as monitor141, may include a sensor probe, such as an Electroencephalogram (EEG)probe, and a communication link that periodically transmitsidentification information and probe data to patient manager 140, sothat the time series of monitored values is stored on patient manager140, enabling the patient manager 140 to form a raw binary alarmindication and/or a physiological variable decision statistic. In anembodiment, patient monitor 141 collects raw sensor information, such asfrom an optical sensor, and performs signal processing, such as velocitymeasurement, forming a physiological variable decision statistic,cumulative summing, trending, wavelet processing, thresholding,computational processing of decision statistics, logical processing ofdecision statistics, preprocessing or signal condition, and/or anycombination of the same, part or all of which may be performed onmonitor 141, manager 140, interface 142, and/or computer system 120, oranother component not depicted in FIG. 1A.

An embodiment of monitor 141 stores user-derived data locally and/orcommunicates data over network 175 to be stored remotely. In anembodiment, manager 140 is wirelessly communicatively coupled to monitor141. Manager 140 may also be embodied as a software application or anapplication operating on a user's mobile device. In an embodiment,manager 140 and monitor 141 are functional components of the samedevice, such as a device comprising a sensor and a user interface. In anembodiment, manager 140 is embodied as a base station, which may alsoinclude functionality for charging monitor 141 or downloadinginformation from monitor 141. Example operating environment 100 furtherincludes computer system 120, which may take the form of a server, whichis communicatively coupled through network 175 to EHR system 160 andstorage 121.

Computer system 120 comprises one or more processors operable to receiveinstructions and process them accordingly, and may be embodied as asingle computing device or multiple computing devices communicativelycoupled to each other. In one embodiment, processing actions performedby system 120 are distributed among multiple locations, such as one ormore local clients and/or one or more remote servers, and may bedistributed across the other components of example operating environment100. For example, a portion of computing system 120 may be embodied onmonitor 141 or manager 140 for performing signal conditioning of themeasured patient variable(s). In one embodiment, system 120 comprisesone or more computing devices, such as a server, desktop computer,laptop, tablet, cloud-computing device, distributed computingarchitecture, and/or a portable computing device, such as a laptop,tablet, ultra-mobile P.C., and/or a mobile phone.

Embodiments of computer system 120 include computer software stack 125,which in some embodiments operates in the cloud as a distributed systemon a virtualization layer within computer system 120, and includesoperating system 129. Operating system 129 may be implemented as aplatform in the cloud, and may be capable of hosting a number ofservices, such as model variables indexing service 122, predictivemodels service 124, computational services 126 (e.g., R systempackages), and lab information system interface 128, for example. Someembodiments of operating system 129 comprise a distributed adaptiveagent operating system. Embodiments of services 122, 124, 126, and 128may run as a local or distributed stack in the cloud, on one or morepersonal computers or servers such as system 120, and/or on a computingdevice running patient manager 140 and user/clinician interface 142. Insome embodiments, interface 142 operates in conjunction with softwarestack 125.

In embodiments, model variables indexing service 122 provide servicesthat facilitate retrieving frequent item sets, extracting databaserecords, and cleaning the values of variables in records. For example,service 122 may perform functions for synonymic discovery, indexing ormapping variables in records, or mapping disparate health systems'ontologies, such as determining that a particular medication frequencyof a first record system is the same as another record system. In someembodiments, these services may invoke computation services 126.Predictive models service 124, in general, may be responsible forproviding multivariable models for predicting CKD, such as thosedescribed in connection with method 200 described with respect to FIG. 2.

Computation services 126 perform statistical software operations, andinclude statistical calculation packages such as, in one embodiment, anR system (the R project for Statistical Computing, which supportsR-packages or modules tailored for specific statistical operations, andwhich is accessible through the Comprehensive R Archive Network (CRAN)at http://cran.r-project.org) or similar services. In an embodiment,computation services 126 include the services or routines which may beembodied as one or more software agents or routines, such as the exampleembodiments of computer program routines illustratively provided in FIG.5 . In some embodiments, computation services 126 may use EHR or labinformation system interface 128, which may provide serial measurementsof urine osmolality, serum sodium concentration, and/or otherphysiological variables. Some embodiments of stack 125 may further useApache Hadoop and Hbase framework (not shown), or similar frameworksoperable for providing a distributed file system, which in someembodiments, facilitate providing access to cloud-based services such asthose provided by Cerner Healthe Intent®. Additionally, some embodimentsof stack 125 may further comprise one or more stream processingservice(s) (not shown). For example, such stream processing service(s)may be embodied using IBM InfoSphere stream processing platform, TwitterStorm stream processing, Ptolemy or Kepler stream processing software,or similar complex event processing (CEP) platforms, frameworks, and/orservices, which may include the user of multiple such stream processingservices (in parallel, serially, or operating independently). Someembodiments of the invention also may be used in conjunction with CernerMillennium®, Cerner CareAware® (including CareAware iBus®), CernerCareCompass®, or similar products and services from various serviceproviders.

Example operating environment 100 also includes storage 121 (or datastore 121), which in some embodiments, may include patient data for acandidate or target patient (and/or information for multiple patients),including raw and processed patient data, variables associated withpatient recommendations, a recommendation knowledge base, recommendationrules, recommendations, recommendation update statistics, an operationaldata store which stores events, frequent item sets (e.g., associationssuch as “X often happens with Y”), and item sets index information,association rule bases, agent libraries, solvers, and solver libraries,and other similar information including data and computer-usableinstructions, patient-derived data, and healthcare provider information,for example. It is contemplated that the term data includes anyinformation that can be stored in a computer storage device or system,such as user-derived data, computer-usable instructions, softwareapplications, and/or other information. In some embodiments, data store121 comprises the data store(s) associated with EHR system 160. Further,although depicted as a single storage data store, data store 121 maycomprise one or more data stores, or may be in the cloud.

In some embodiments, computer system 120 is a computing system made upof one or more computing devices. In some embodiments, computer system120 includes one or more software agents, and in another embodiment,includes an adaptive multi-agent operating system. However, it should beappreciated that computer system 120 may also take the form of anadaptive single agent system or a non-agent system. Computer system 120may be a distributed computing system, a data processing system, acentralized computing system, a single computer such as a desktop orlaptop computer, and/or a networked computing system.

Turning briefly to FIG. 1B, an exemplary embodiment of computing system900, including software instructions for storage of data and programs incomputer-readable media, is provided, in accordance with an embodimentof the present disclosure. Computing system 900 is representative of asystem architecture that is suitable for computer systems such ascomputing system 120. One or more central processing units (CPUs), suchas CPU 901, are provided with internal memory for storage ofinformation, and are coupled to north bridge device 902, allowing CPU901 to store instructions and data elements in system memory 915, ormemory associated with graphics card 910, which is coupled to display911. Bios flash ROM 940 couples to north bridge device 902. South bridgedevice 903 connects to north bridge device 902, allowing CPU 901 tostore instructions and data elements in disk storage 931, which maycomprise a fixed disk or USB disk, or to make use of network 933 forremote storage. User I/O device 932, which may comprise a communicationdevice, a mouse, a touchscreen, a joystick, a touch stick, a trackball,and/or keyboard, is coupled to CPU 901 through south bridge 903. Thesystem architecture depicted in FIG. 1B is provided as one example ofany number of suitable computer architectures, such as computingarchitectures that support local, distributed, and/or cloud-basedsoftware platforms, and are suitable for supporting computing system120.

Turning now to FIG. 2 , a block diagram of an exemplary method 200 forpredicting CKD progression is provided, in accordance with an embodimentof the present disclosure. At block 210, recent lab measurements ofurine osmolality and serum sodium are received, along with correspondingdate-time stamps. These measurements may be received from one or moreEHRs, such as EHR 160 or from monitor 141, and may correspond to asubject for which an analysis of CKD progression is performed. At block220, an AVP V2 antagonist, such as tolvaptan, is administered. At block230, an amount of time is allowed to elapse to measure the changefollowing the administering of the AVP V2 antagonist. An exemplaryelapsed time period may be 4-6 hours (this may also be considered apredetermined or preconfigured time period). The elapsed time may varybased on a particular AVP V2 antagonist that is used. At block 240, ameasurement of a new urine osmolality and corresponding date-time stampis received, following the AVP V2 antagonist challenge. Blocks 210-240are intended to accumulate the serial measurements of urine osmolalityand serum sodium and corresponding data-time stamps for generating atime series of the measurements for each variable. Some embodiments ofmethod 200 accumulate at least a pair of values for each of thesevariables.

At block 250, any variable value or time point range affected by erroror measurement artifact is censored. For example, in some situations, itmay be necessary to weed-out measurements that are too close together(e.g., taken at nearly the same time) or too far apart, such that datacomparison is not useful or desirable. At block 260, the time series ofserum sodium and urine osmolality is transformed, such as described inthe example computer program routines illustratively provided in FIG. 5. The transformation may include, for example, determining velocityand/or doubling-time for each of the variables obtained. Thus, someembodiments of block 260 may be carried out using the example computerprogram routines illustratively provided in FIG. 5 , which may beimplemented using the R-system packages, as described in relation toFIG. 1A.

At block 270, a predicted probability of agent efficacy in retarding CKDprogression is calculated. Some embodiments of block 270 may be carriedout using the example computer program routines illustratively providedin FIG. 5 , which may be implemented using the R system packagesdescribed in relation to FIG. 1A. Some embodiments of block 270 may usea logistic regression model for determining the prediction. Otherembodiments of block 270 may use a neural network, support vectormachine model, and/or other classifier model. In some embodiments, theprobability determined in step 270 is a score corresponding to alikelihood of CKD progression.

At block 280, one or more clinicians or caregivers may be notified ofthe efficacy of the treatment for a particular patient. In someembodiments of block 280, a threshold is applied to theprobability/score determined in block 270, and if the threshold issatisfied, then clinicians and/or caregivers are notified and/orinstructed for a particular intervention or other action (e.g.,preparing a treatment plan, executing a patient visit, preparingadditional medication or therapeutic treatment, etc.). The threshold maybe set by a clinician, health care provider, and/or may be determinedempirically. At block 285, it is determined whether to continuereceiving additional measurements of urine osmolality and/or serumsodium concentration and corresponding date-time stamps. If so, thenmethod 200 proceeds to block 210, as described above, and if no, thenthe method 200 ends.

With reference to FIGS. 3-5 , an exemplary embodiment of the technologyreduced to practice for a time series multi-variable properties-basedprediction and prevention of CKD progression is provided, in accordancewith an embodiment of the present disclosure. In this example, recordshave been retrieved from a patient health records data warehouse, whichis derived from Cerner electronic health records (EHR) from 100% ofepisodes of care that are incident upon the participating healthinstitutions. The personally identifiable information was removed inconformance with U.S. HIPAA laws and regulations, and the de-identifieddata has been stored in a separate, secure database. A total of 40,181ambulatory patient records, which contained four or more date-timestamped values for each of a variety of laboratory and physiologicparameters that were contemporaneous with the nephrology clinic episodesare provided.

Two vasopressin antagonists (‘vaptans’) are now marketed for thetreatment of euvolemic (Europe) or euvolemic and hypervolemic (UnitedStates) hyponatremia. These are conivaptan for intravenous use andtolvaptan for oral use. Although their specificity and effectiveness arewell-established, their indications are not. At present, it is not knownwhich symptoms of hyponatremia and which degree of hyponatremia shouldserve as indications for vaptans. It is emphasized that vaptans areeffective only in the presence of ADH derangement (‘SIADH’), but not inthe syndrome of nephrogenic antidiuresis. Vaptans decrease the highmortality and morbidity associated with hyponatremia. This is therationale that presently justifies the cost of chronic vaptan therapy inheart failure CKD. The optimal vaptan regimen (e.g., timing ofinitiation, dose, dose-escalation/titration, etc.) in Stages 3-4 CKD iscurrently not established by controlled clinical trials. However, largeobservational EHR-derived de-identified datasets such as Cerner HealthFacts® data warehouse enable (a) to discover context-specific regimensthat are safe and effective in delaying progression to Stage 5 CKD(ESRD), requiring dialysis or transplantation, and (b) to developpredictive mathematical models that identify who will benefit from theseregimens and who will not, irrespective of whether hyponatremia ispresent or, if it is present, whether it is severe or symptomatic.

Hyponatremia is 35% prevalent in Stage 3 CKD and 56% prevalent in Stage4 CKD. A multi-variable predictive model and vaptan regimen offers theopportunity to deliver enhanced clinical outcomes in terms of improvedquality of life (QoL), reduced mortality and morbidity, and slowing ofCKD progression. For Fresenius, it represents market growth in earlierstage CKD populations. Although the precise mechanism of resistance toAVP antagonists is unknown, observational data confirm thatconcentrating and diluting ability in the collecting ducts is necessary,but not sufficient, for the efficacy of AVP antagonists, and it is oftenimpaired in the elderly or those with CKD that has already advanced toStage 5.

Among the prevalent Stage 3 and Stage 4 CKD patients in the HealthFacts® cohort, a total of 49 subjects were treated off-label withtolvaptan. Among these, 28 (57.1%) of the 49 patients were responsive tothe AVP V2 antagonist. In tolvaptan-exposed patients, the duration oftolvaptan treatment ranged from 240 days to 1,256 days. Although thecohort available was small, Cox proportional hazards regression suggestsa substantial retardation of progression to Stage 5 CKD(tolvaptan-exposed vs. controls: 1.9±1.4 years). In tolvaptan-exposedpatients, the duration of tolvaptan treatment ranged from 240 days to1,256 days. Staging of CKD was established as shown in FIG. 3 , which isin accordance with K/DOQI and related guidelines.

In some embodiments, a random Forest package implementation of theRandom Forest (RF) method was used to identify a subset of parameterswhose time-dependent changes in value were associated with futureemergence of Stage 5 CKD with a forward time-horizon of 5 years. Twoparameters—baseline pre-challenge urine osmolality and percentagedecrease in urine osmolality within 4 to 6 hours after administering a15 mg tolvaptan challenge—were determined to be statisticallysignificant and were retained for subsequent modeling via logisticregression. As shown in FIG. 4 , the Receiver Operating Characteristics(ROC) area under the curve in the final model was 0.95.

The as-treated dataset contained measurements of the parameters taken inthe course of conventional ordering practices in an ambulatorynephrology clinic setting. Electrolytes, blood urea nitrogen, andcreatinine were routinely measured on each clinic visit. However,certain urine chemistry measurements, such as urine sodium and urineosmolality, were measured infrequently and only in a subset of the CKDcohort. As such, it was uncommon to have more than a few measurements ofsome of the parameters during the 5-year period. Such a low frequency ofmeasurement is not a major impediment to the successful accomplishmentof the predictive aim of the present technology. However, in someembodiments, it may be desirable to acquire more frequent measurements,such as quarterly or at other suitable intervals, particularly inpopulations whose CKD etiology is such as to have elevated risk ofaccelerated progression to Stage 5 CKD, such as those with polycystickidney disease (PKD) or IgA nephropathy (Berger's Disease).

Further findings in this convenience cohort included the discovery thatsome of the patients analyzed were prescribed off-label concomitantTNF-alpha inhibitor treatment for at least a portion of the time whentolvaptan was prescribed. The TNF-alpha inhibitor treatment was deemedto be ‘off-label’ insofar as there was no other known clinicalindication present, such as rheumatoid arthritis, inflammatory boweldisease, psoriasis, or other autoimmune disease. In this sub-cohort, CKDprogression was slower than in patients exposed only to tolvaptan. Thenumbers of such subjects were too small to reach statisticalsignificance. However, this unexpected finding suggests the appealingpossibility that a therapeutic challenge with AVP V2 antagonist mightindirectly be predictive of responsiveness to multidrug regimens or tomonotherapy with agents that are not AVP V2 antagonists, and whosemechanisms of action are unrelated to AVP V2 antagonism or hyponatremia.

These temporal patterns are of such complexity and variability that itwould be beyond the capability of a human being to examine the values ofthe laboratory results for urine osmolality tests and determine aprediction for progression to Stage 5 CKD that has not yet materialized.Through some embodiments of this disclosure, it is shown that temporalchanges in urine osmolality values—either jointly or separately—canserve as a reliable composite leading indicator of (a) therapeuticefficacy of an AVP V2 antagonist in retarding CKD progression, and (b)of subsequent progression to Stage 5 CKD. Another aspect of thetechnology concerns (a) determining at least one temporal property of apost-challenge urine osmolality time series, such as a percentage changefrom baseline or velocity, (b) the transformation of the at least onetemporal property to an integer score, (c) the combining of the evidencevia a multi-variable predictive model, such as a logistic regressionequation to form a quantitative probability of Stage 5 CKD materializingwithin a subsequent time interval, and (d) the rendering of thepredicted probability to one or more human decision-makers in thecontext of an electronic health record information system.

Turning now to FIG. 6 , a block diagram of a method 600 for predictingthe progression of chronic kidney disease (CKD) in a patient isprovided, in accordance with an embodiment of the present disclosure. Ata block 610, one or more physiologic variables of the patient aremeasured at a plurality of time stamped data points, the one or morephysiologic variables associated with kidney function (e.g., urineosmolality or serum sodium). At a block 612, an AVP V2 antagonist agent(e.g., tolvaptan, conivaptan, etc.) is administered to the patient. At ablock 614, the one or more physiologic variables are measured, after apredetermined time period has elapsed (e.g., 4-6 hours), at one or morepost-AVP V2 administration time stamped data points. At a block 616, aserial measurement time series is compiled comprising the plurality oftime stamped data points and the one or more post-AVP V2 administrationtime stamped data points. At a block 618, a predicted probability thatthe AVP V2 antagonist agent will retard the progression of the CKD iscalculated by transforming the serial measurement time series (e.g.,using a logistic regression model, neural network, or support vectormachine model). At a block 620, utilizing the predicted probability, anaction is evoked corresponding to treatment of the patient (e.g.,providing a notification to a clinician, automatically generating atreatment plan for the patient, scheduling resources or personnel fortreatment, entering information into a patient record, such as an EHR,etc.).

Turning now to FIG. 7 , a block diagram of an exemplary method 700 forpredicting the progression of chronic kidney disease (CKD) in a patientis provided, in accordance with an embodiment of the present disclosure.At a block 710, measurements of one or more physiologic variables forthe patient at a plurality of time stamped data points are received, theone or more physiologic variables associated with kidney function. At ablock 712, an indication that an AVP V2 antagonist agent has beenadministered to the patient is received. At a block 714, after apredetermined time period has elapsed, measurements of the one or morephysiologic variables at one or more post-AVP V2 administration timestamped data points are received. At a block 716, a serial measurementtime series comprising the plurality of time stamped data points and theone or more post-AVP V2 administration time stamped data points iscompiled. At a block 718, a predicted probability of progression of theCKD is calculated by transforming the serial measurement time series. Ata block 720, it is determined if the predicted probability exceeds apredetermined threshold. At a block 722, upon determining that thepredicted probability exceeds the predetermined threshold, an actioncorresponding to treatment of the patient is evoked.

Embodiment 1: A method for predicting the progression of Chronic KidneyDisease (CKD) in a patient. The method comprises measuring one or morephysiologic variables of the patient at a plurality of time stamped datapoints, the one or more physiologic variables associated with kidneyfunction; administering an AVP V2 antagonist agent to the patient;measuring, after a predetermined time period has elapsed, the one ormore physiologic variables at one or more post-AVP V2 administrationtime stamped data points; compiling a serial measurement time seriescomprising the plurality of time stamped data points and the one or morepost-AVP V2 administration time stamped data points; calculating apredicted probability that the AVP V2 antagonist agent will retard theprogression of the CKD by transforming the serial measurement timeseries; and utilizing the predicted probability, evoking an actioncorresponding to treatment of the patient.

Embodiment 2: The method of embodiment 1, further comprising censoringany of the plurality of time stamped data points affected by at leastone of error and measurement artifact.

Embodiment 3: The method of any of embodiments 1-2, wherein transformingthe serial measurement time series comprises using at least one of thefollowing on the serial measurement time series: a logistic regressionmodel, a neural network, and a support vector machine model.

Embodiment 4: The method of any of embodiments 1-3, further comprisingtransforming the plurality of time stamped data points by performing atleast one of determining a velocity of the serial measurement timeseries and determining a doubling-time for each of the measured one ormore physiologic variables in the serial measurement time series.

Embodiment 5: The method of any of embodiments 1-4, wherein the one ormore physiologic variables comprise at least one of urine osmolality ofthe patient and serum sodium of the patient.

Embodiment 6: The method of any of embodiments 1-5, wherein theplurality of time stamped data points comprises two time stamp datapoints.

Embodiment 7: The method of any of embodiments 1-6, wherein thepredetermined time period is between 4 and 6 hours inclusive.

Embodiment 8: The method of any of embodiments 1-7, wherein the measuredone or more physiologic variables from the plurality of time stampeddata points are received from an Electronic Health Record (EHR).

Embodiment 9: The method of any of embodiments 1-8, wherein the AVP V2antagonist agent comprises at least one of tolvaptan, conivaptan, andserum sodium concentration.

Embodiment 10: One or more computer-readable media havingcomputer-executable instructions embodied thereon that, when executed,facilitate a method for predicting the progression of chronic kidneydisease (CKD) in a patient. The method comprises receiving measurementsof one or more physiologic variables for the patient at a plurality oftime stamped data points, the one or more physiologic variablesassociated with kidney function; receiving an indication that an AVP V2antagonist agent has been administered to the patient; receivingmeasurements, after a predetermined time period has elapsed, of the oneor more physiologic variables at one or more post-AVP V2 administrationtime stamped data points; compiling a serial measurement time seriescomprising the plurality of time stamped data points and the one or morepost-AVP V2 administration time stamped data points; calculating apredicted probability of progression of the CKD by transforming theserial measurement time series; determining if the predicted probabilityexceeds a predetermined threshold; and upon determining that thepredicted probability exceeds the predetermined threshold, evoking anaction corresponding to treatment of the patient.

Embodiment 11: The computer-readable media of embodiment 10, wherein theaction corresponding to treatment of the patient comprises at least oneof initiating a signal that causes an alert to be presented to aclinician, initiating a signal for a plan of care to be initiated forthe patient, preparing a treatment plan for the patient, automaticallyscheduling a caregiver to provide therapeutic treatment to the patient,and modifying or generating a healthcare computer program for treatingthe patient.

Embodiment 12: The computer-readable media of any of embodiments 10-11,wherein transforming the serial measurement time series comprises usingat least one of the following on the serial measurement time series: alogistic regression model, a neural network, and a support vectormachine model.

Embodiment 13: The computer-readable media of any of embodiments 10-12,wherein the one or more physiologic variables comprises at least one ofurine osmolality of the patient and serum sodium of the patient.

Embodiment 14: The computer-readable media of any of embodiments 10-13,wherein the method further comprises censoring any value or range of theserial measurement time series affected by error or measurementartifact.

Embodiment 15: The computer-readable media of any of embodiments 10-14,wherein the predetermined time period comprises between 4 and 6 hoursinclusive.

Embodiment 16: The computer-readable media of any of embodiments 10-15,wherein the one or more physiologic variables from the plurality of timestamped data points are received from an Electronic Health Record (EHR).

Embodiment 17: The computer-readable media of any of embodiments 10-16,wherein the AVP V2 antagonist agent comprises at least one of tolvaptan,conivaptan, and serum sodium concentration.

Embodiment 18: A system for predicting the progression of chronic kidneydisease (CKD) in a patient. The system comprises one or more processers,one or more sensors configured to measure one or more physiologicvariables for the patient, the one or more physiologic variablesassociated with kidney function; and computer storage memory havingcomputer-executable instructions stored thereon that, when executed bythe processor, implement a method comprising receiving measurements ofthe one or more physiologic variables for the patient at a plurality oftime stamped data points; receiving an indication that an AVP V2antagonist agent has been administered to the patient; receivingmeasurements, after a predetermined time period has elapsed, of the oneor more physiologic variables at one or more post-AVP V2 administrationtime stamped data points; compiling a serial measurement time seriescomprising the plurality of time stamped data points and the one or morepost-AVP V2 administration time stamped data points; calculating apredicted probability of progression of the CKD of the patient bytransforming the serial measurement time series; determining if thepredicted probability exceeds a predetermined threshold; and upondetermining that the predicted probability exceeds the predeterminedthreshold, evoking an action corresponding to treatment of the patient.

Embodiment 19: The system of embodiment 18, wherein the actioncorresponding to treatment of the patient comprises at least one ofinitiating a signal that causes an alert to be presented to a clinician,initiating a signal for a plan of care to be initiated for the patient,automatically scheduling a caregiver to provide therapeutic treatment tothe patient, and modifying or generating a healthcare computer programfor treating the patient.

Embodiment 20: The system of any of embodiments 18-19, whereintransforming the serial measurement time series comprises using at leastone of the following on the serial measurement time series: a logisticregression model, a neural network, and a support vector machine model.

Many different arrangements of the various components depicted, as wellas use of components not shown, are possible without departing from thespirit and scope of the present disclosure. Embodiments of the presentinvention have been described with the intent to be illustrative ratherthan restrictive. Alternative embodiments will become apparent to thoseskilled in the art that do not depart from its scope. A skilled artisanmay develop alternative means of implementing the aforementionedimprovements without departing from the scope of the present invention.

It will be understood that certain features and sub-combinations are ofutility and may be employed without reference to other features andsub-combinations and are contemplated within the scope of the claims.Not all steps listed in the various figures need be carried out in thespecific order described. Accordingly, the scope of the invention isintended to be limited only by the following claims.

What is claimed is:
 1. A method for predicting a progression of ChronicKidney Disease (CKD) in a patient, the method comprising: measuring oneor more physiologic variables of the patient at a plurality of timestamped data points, the one or more physiologic variables associatedwith kidney function; administering one or more agents to the patient;measuring, after a predetermined time period has elapsed, the one ormore physiologic variables at one or more post-administration of one ormore agents time stamped data points; compiling a serial measurementtime series comprising the plurality of time stamped data points and theone or more post-agent administration time stamped data points;calculating a predicted probability that the one or more agents willretard the progression of the CKD by transforming the serial measurementtime series wherein transforming the plurality of time stamped datapoints comprises using at least one of the following on the serialmeasurement time series: a logistic regression model; a neural network;and a support vector machine model utilizing the predicted probability,evoking an action corresponding to treatment of the patient.
 2. Themethod of claim 1, further comprising censoring any of the plurality oftime stamped data points affected by at least one of: error; andmeasurement artifact.
 3. The method of claim 1, further comprisingtransforming the plurality of time stamped data points by performing atleast one of: determining a velocity of the serial measurement timeseries; and determining a doubling-time for each of the measured one ormore physiologic variables in the serial measurement time series.
 4. Themethod of claim 1, wherein the one or more agents comprises an ArginineVasopressin (AVP) V2 antagonist agent.
 5. The method of claim 4, whereinthe AVP V2 antagonist agent comprises at least one of: tolvaptan;conivaptan; and serum sodium concentration.
 6. The method of claim 1,wherein the one or more physiologic variables comprise at least one of:urine osmolality of the patient; and serum sodium of the patient.
 7. Themethod of claim 1, wherein the plurality of time stamped data pointscomprises two time stamp data points.
 8. The method of claim 1, whereinthe predetermined time period is between 4 and 6 hours inclusive.
 9. Themethod of claim 1, wherein the measured one or more physiologicvariables from the plurality of time stamped data points are receivedfrom an Electronic Health Record (EHR).
 10. One or more non-transitorycomputer-readable media having computer-executable instructions embodiedthereon that, when executed, facilitate a method for predicting aprogression of Chronic Kidney Disease (CKD) in a patient the methodcomprising: receiving measurements of one or more physiologic variablesfor the patient at a plurality of time stamped data points, the one ormore physiologic variables associated with kidney function; receiving anindication that one or more agents have been administered to thepatient; receiving measurements, after a predetermined time period haselapsed, of the one or more physiologic variables at one or morepost-agent administration time stamped data points; compiling a serialmeasurement time series comprising the plurality of time stamped datapoints and the one or more post-agent administration time stamped datapoints; calculating a predicted probability that the one or more agentswill retard the progression of the CKD by transforming the serialmeasurement time series wherein transforming the plurality of timestamped data points comprises using at least one of the following on theserial measurement time series: a logistic regression model; a neuralnetwork; and a support vector machine model; determining if thepredicted probability exceeds a predetermined threshold; and upondetermining that the predicted probability exceeds the predeterminedthreshold, evoking an action corresponding to treatment of the patient.11. The non-transitory computer-readable media of claim 10, wherein theaction corresponding to treatment of the patient comprises at least oneof: initiating a signal that causes an alert to be presented to aclinician; initiating a signal for a plan of care to be initiated forthe patient; preparing a treatment plan for the patient; automaticallyscheduling a caregiver to provide therapeutic treatment to the patient;and modifying or generating a healthcare computer program for treatingthe patient.
 12. The non-transitory computer-readable media of claim 10,wherein the one or more agents comprises an Arginine Vasopressin (AVP)V2 antagonist agent.
 13. The non-transitory computer-readable media ofclaim 10, wherein the one or more physiologic variables comprises atleast one of: urine osmolality of the patient; and serum sodium of thepatient.
 14. The non-transitory computer-readable media of claim 10,wherein the method further comprises censoring any value or range of theserial measurement time series affected by error or measurementartifact.
 15. The non-transitory computer-readable media of claim 10,wherein the predetermined time period comprises between 4 and 6 hoursinclusive.
 16. The non-transitory computer-readable media of claim 10,wherein the one or more physiologic variables from the plurality of timestamped data points are received from an Electronic Health Record (EHR).17. The non-transitory computer-readable media of claim 10, wherein theAVP V2 antagonist agent comprises at least one of: tolvaptan;conivaptan; and serum sodium concentration.
 18. A system for predictingthe progression of Chronic Kidney Disease (CKD) in a patient, the systemcomprising: one or more processers; one or more sensors configured tomeasure one or more physiologic variables for the patient, the one ormore physiologic variables associated with kidney function; and computerstorage memory having computer-executable instructions stored thereonthat, when executed by the processor, implement a method comprising:receiving measurements of the one or more physiologic variables for thepatient at a plurality of time stamped data points; receiving anindication that one or more agents have been administered to thepatient; receiving measurements, after a predetermined time period haselapsed, of the one or more physiologic variables at one or morepost-agent administration time stamped data points; compiling a serialmeasurement time series comprising the plurality of time stamped datapoints and the one or more post-agent administration time stamped datapoints; censoring any of the plurality of time stamped data pointsaffected by at least one of: error; and measurement artifact;calculating a predicted probability of progression of the CKD of thepatient by transforming the serial measurement time series; determiningif the predicted probability exceeds a predetermined threshold; and upondetermining that the predicted probability exceeds the predeterminedthreshold, evoking an action corresponding to treatment of the patient.19. The system of claim 18, wherein the action corresponding totreatment of the patient comprises at least one of: initiating a signalthat causes an alert to be presented to a clinician; initiating a signalfor a plan of care to be initiated for the patient; automaticallyscheduling a caregiver to provide therapeutic treatment to the patient;and modifying or generating a healthcare computer program for treatingthe patient.
 20. The system of claim 18, wherein transforming the serialmeasurement time series comprises using at least one of the following onthe serial measurement time series: a logistic regression model; aneural network; and a support vector machine model.